<p>A large number of hydrological forecasts are carried out daily by the hydro-meteorologists of the french electricity production agency (EDF). These forecasts are based on a MORDOR hydrological model [Boy, 1996]. Since its development, this model has been noted for its performance [Mathevet, 2005], and a new more advanced version proposing a semi-distributed (or SD) structure improves the quality of the simulations [Garavaglia et al., 2017].</p><p>However, many uncertainties such as calibration errors, unavailable observations, and the uncertainties linked to the data used as forcing for the model can have a very significant impact on the quality of the results. Data assimilation is a relevant method for reducing the uncertainties of forcings and then obtain better quality simulations. Previous studies show a gain in the contribution of a variational assimilation to initialize a semi-distributed hydrological model [Lee et al., 2011], but the variational methods are less effective with non-linear behaviors. Therefore the ensemble methods are more widely adopted, as the ensemble Kalman filter (or EnKF) assimilation method which can be found in various studies ([Han et al., 2012], [Clark et al., 2008], [Xie and Zhang, 2010], [Slater and Clark, 2006], [Chen et al., 2011], [Alvarez-Garreton et al., 2015]).</p><p>As part of our study, a particle filter has been implemented as an assimilation scheme in the semi-distributed hydrological model MORDOR-SD. Several types of observations, such as the flow at the outlet of the watershed or the snow stock, were used in this assimilation system. Some sensitivities experiments on the various parameters specific to the system as well as on the choice of the observations to be taken into account were carried out. This study will show the benefits obtained from the assimilation of in situ data on the quality of the simulations as well as on the forecasts. Performed in many different areas (the study covers several watersheds), the analysis of observation errors and the construction of a specific observation error model brings an additional benefit in the quality of the results.</p><p>&#160;</p>
Merging particle filter for sequential data assimilation